Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable tool to localize, characterize, and evaluate anomalous prostate tissue. Ultrafast gradient-echo acquisitions of MRI volumes are generated at regular time intervals while the patient receives a paramagnetic contrast agent. The result is a time series where each voxel exhibits a particular behavior of contrast uptake (wash-in) and posterior wash-out. In this work, a segmentation method is proposed that groups together neighboring voxels with similar contrast wash-out responses, using a variant of the region growing GrowCut cellular automaton algorithm, that evolves iteratively according to the relationship of a cell to its neighboring cells. Initially, seed cells are determined through morphological filters that identify homogeneous regions in the volume that are representatives of the clinically relevant types of tissues in the prostate. Each cell is characterized by three parameters: a label that identifies the type of tissue in the associated voxel, a vector with the values of the DCE-MRI time series, and a strength coefficient with values between 0 and 1 that represents the probability with which the cell belongs to its assigned label. Every non-seed cell can modify its state; this occurs when each cell is attacked by its neighbors with a strength of attack that is inversely proportional to the similarity of the values of the time series between the cells. If the strength of the attacked cell is less than the strength of the attack of one of its neighbors, its state changes to the one of the attacking cell. The attacked cell also updates its strength making it equal to the strength of the attack with which it was conquered. To perform a clinical validation of the resulting segmentations, we used various cases from the database of The Cancer Imaging Archive (TCIA), National Cancer Institute (NCI).
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；University of Central Florida（中佛罗里达大学）；Chung Yuan Christian University（中原大学）；New York Presbyterian Hospital（纽约长老会医院）；Weill Cornell Medical College（威尔康奈尔医学院）
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；BC Cancer（BC癌症中心）
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；Siemens Medical Solutions USA（美国西门子医疗解决方案公司）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in mpMRI
School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Azzaytuna University（阿扎伊图纳大学）；Faculty of Science & Engineering (Physics), Swansea University（斯旺西大学科学与工程学院（物理系））
